このコースについて

293,149 最近の表示

受講生の就業成果

32%

コース終了後に新しいキャリアをスタートした

34%

コースが具体的なキャリアアップにつながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
約26時間で修了
英語
字幕:英語, 韓国語

学習内容

  • Understand how text is handled in Python

  • Apply basic natural language processing methods

  • Write code that groups documents by topic

  • Describe the nltk framework for manipulating text

習得するスキル

Natural Language Toolkit (NLTK)Text MiningPython ProgrammingNatural Language Processing

受講生の就業成果

32%

コース終了後に新しいキャリアをスタートした

34%

コースが具体的なキャリアアップにつながった
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
約26時間で修了
英語
字幕:英語, 韓国語

提供:

ミシガン大学(University of Michigan) ロゴ

ミシガン大学(University of Michigan)

シラバス - 本コースの学習内容

コンテンツの評価Thumbs Up92%(4,684 件の評価)Info
1

1

8時間で修了

Module 1: Working with Text in Python

8時間で修了
5件のビデオ (合計56分), 4 readings, 3 quizzes
5件のビデオ
Handling Text in Python18 分
Regular Expressions16 分
Demonstration: Regex with Pandas and Named Groups5 分
Internationalization and Issues with Non-ASCII Characters12 分
4件の学習用教材
Course Syllabus10 分
Help us learn more about you!10 分
Notice for Auditing Learners: Assignment Submission10 分
Resources: Common issues with free text10 分
2の練習問題
Practice Quiz8 分
Module 1 Quiz12 分
2

2

6時間で修了

Module 2: Basic Natural Language Processing

6時間で修了
3件のビデオ (合計36分)
3件のビデオ
Basic NLP tasks with NLTK16 分
Advanced NLP tasks with NLTK16 分
2の練習問題
Practice Quiz4 分
Module 2 Quiz10 分
3

3

7時間で修了

Module 3: Classification of Text

7時間で修了
7件のビデオ (合計94分)
7件のビデオ
Identifying Features from Text8 分
Naive Bayes Classifiers19 分
Naive Bayes Variations4 分
Support Vector Machines24 分
Learning Text Classifiers in Python15 分
Demonstration: Case Study - Sentiment Analysis9 分
1の練習問題
Module 3 Quiz14 分
4

4

6時間で修了

Module 4: Topic Modeling

6時間で修了
4件のビデオ (合計58分), 2 readings, 3 quizzes
4件のビデオ
Topic Modeling8 分
Generative Models and LDA13 分
Information Extraction18 分
2件の学習用教材
Additional Resources & Readings10 分
Post-Course Survey10 分
2の練習問題
Practice Quiz4 分
Module 4 Quiz10 分

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Python 応用データサイエンス専門講座について

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate....
Python 応用データサイエンス

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